Multi-access edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in a low performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where a MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost, while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

Computing services for the Internet-of-Things (IoT) play a vital role for widespread IoT deployment. A hierarchy of Edge-Cloud publish/subscribe (pub/sub) broker overlay networks that support latency-sensitive IoT applications in a scalable manner is introduced. In addition, we design algorithms to cluster edge pub/sub brokers based on topic similarities and geolocations to enhance data dissemination among end-to-end IoT devices. The proposed model is designed to provide low delay data dissemination and effectively save network traffic among brokers. In the proposed model, IoT devices running pub/sub client applications periodically send collected data, organized as a hierarchy of topics, to their closest edge pub/sub brokers. Then, the data are processed/analyzed at edge nodes to make controlling decisions promptly replying to the IoT devices and/or aggregated for further delivery to other interested edge brokers or to cloud brokers for long-term processing, analysis, and storage. Extensive simulation results demonstrate that our proposal achieves the best data delivery latency compared to two baseline schemes, a classical Cloud-based pub/sub scheme and an Edge-Cloud pub/sub scheme. Considering the similar Edge-Cloud technique, the proposed scheme outperforms PubSubCoord-alike in terms of relay traffic ratio among brokers. Therefore, our proposal can adapt well to support wide-scale latency-sensitive IoT applications.

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.